Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add danielrosehill/Claude-AI-Video-Producer-Plugin --skill model-researchergit clone --depth 1 https://github.com/danielrosehill/Claude-AI-Video-Producer-PluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/danielrosehill/claude-ai-video-producer-plugin/model-researcher)<a href="https://agentmods.dev/skills/danielrosehill/claude-ai-video-producer-plugin/model-researcher"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-ai-video-producer-plugin/model-researcher/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/danielrosehill/claude-ai-video-producer-plugin/model-researcher"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-ai-video-producer-plugin/model-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.00665 |
| Opus 5 | $0.00057 | $0.00332 |
| Sonnet 5 | $0.00023 | $0.00133 |
| Haiku 4.5 | $0.00012 | $0.00067 |
Grade A, and why
model-researcher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model researcher
You shortlist and recommend AI models for a given video generation workload.
Inputs
- The workload the user wants to fill (e.g. "image-to-video, 9:16, ~6s, photorealistic faces").
brief/creative-brief.mdfor resolution / aspect / duration / tone constraints.brief/tools-and-models.mdfor what's already chosen (don't re-recommend).- Optional: a budget cap per shot from the user.
Sources
- fal.ai catalogue —
fal.ai/modelsand the model pages. Use the fetch / web tools. - Replicate explore —
replicate.com/exploreand individual model pages. - Provider-direct: Runway, Kling, Pika Labs, Hedra, Sync.so, ElevenLabs, OpenAI for models not on fal/replicate.
- Recent independent comparisons (Reddit r/aivideo, AInVFX, fxguide) — flag as opinion, not fact.
Always note the date you pulled the information; this space turns over fast.
What to capture per candidate
- Name + provider:
- Workload fit: <how well does it suit the task>
- Quality (reputation): <strong / mid / weak> + 1-line reasoning
- Price: <per-second / per-image / per-call>
- Max output: <duration, resolution>
- Aspect ratios: <list>
- Notable failure modes: <e.g., "drifts on long shots", "weak hands", "no lip-sync">
- Provider: <fal / replicate / direct>
- Date checked: <YYYY-MM-DD>
Output
- A short list — 3 to 5 candidates — saved to
research/models-<workload>-<YYYY-MM-DD>.md. - A recommendation with reasoning: best overall, cheapest acceptable, highest quality if budget allows.
- On user approval, update
brief/tools-and-models.md— replace or append the relevant slot. Keep a "Considered but rejected" footer with one line per dropped candidate.
Discipline
- Don't trust marketing pages on provider sites for failure modes. Look for community examples and known issues.
- Flag pricing where the model bills per second of output vs per second of generation time — they're very different.
- If a model is only on the provider's own platform (no fal/replicate proxy), call out the credential implication — the user needs a separate account/key.
- Don't recommend a model the project's selected MCP servers can't reach without the user adding a new server. Surface that as a separate decision.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 52 lines · 115 tokens per session scan A 13c3efe04be7
model-researcher is a skill published in the GitHub repository danielrosehill/Claude-AI-Video-Producer-Plugin (4 stars, last pushed 4mo ago), licensed MIT. It adds 115 tokens to every session and 665 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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